Comparison between cross-correlation and optical flow methods for patient motion detection in SPECT

2002 ◽  
Author(s):  
R. Noumeir ◽  
G.E. Mailloux ◽  
R. Lemieux
Keyword(s):  
Optical Flow ◽  
Motion Detection ◽  
Cross Correlation ◽  
Patient Motion

scholarly journals Influence of patient motion on quantitative accuracy in cardiac 15O-water positron emission tomography

2021 ◽  
Author(s):  
Jonny Nordström ◽  
Hendrik J. Harms ◽  
Tanja Kero ◽  
Jens Sörensen ◽  
Mark Lubberink
Keyword(s):  
Motion Detection ◽  
Detection Algorithm ◽  
Clinical Effects ◽  
Average Deviation ◽  
Patient Motion ◽  
Motion Data ◽  
Positron Emission ◽  
Quantitative Accuracy ◽  
Pet Ct ◽  
Using Data

Abstract Background Patient motion is a common problem during cardiac PET. The purpose of the present study was to investigate to what extent motions influence the quantitative accuracy of cardiac 15O-water PET/CT and to develop a method for automated motion detection. Method Frequency and magnitude of motion was assessed visually using data from 50 clinical 15O-water PET/CT scans. Simulations of 4 types of motions with amplitude of 5 to 20 mm were performed based on data from 10 scans. An automated motion detection algorithm was evaluated on clinical and simulated motion data. MBF and PTF of all simulated scans were compared to the original scan used as reference. Results Patient motion was detected in 68% of clinical cases by visual inspection. All observed motions were small with amplitudes less than half the LV wall thickness. A clear pattern of motion influence was seen in the simulations with a decrease of myocardial blood flow (MBF) in the region of myocardium to where the motion was directed. The perfusable tissue fraction (PTF) trended in the opposite direction. Global absolute average deviation of MBF was 3.1% ± 1.8% and 7.3% ± 6.3% for motions with maximum amplitudes of 5 and 20 mm, respectively. Automated motion detection showed a sensitivity of 90% for simulated motions ≥ 10 mm but struggled with the smaller (≤ 5 mm) simulated (sensitivity 45%) and clinical motions (accuracy 48%). Conclusion Patient motion can impair the quantitative accuracy of MBF. However, at typically occurring levels of patient motion, effects are similar to or only slightly larger than inter-observer variability, and downstream clinical effects are likely negligible.

Optical-Flow Algorithm for Near-Wake Analysis of Axisymmetric Blunt-Based Body at Low-Speed Conditions

2020 ◽  
Vol 142 (11) ◽  
Author(s):  
The Hung Tran ◽  
Lin Chen
Keyword(s):  
Optical Flow ◽  
Cross Correlation ◽  
Transient Flow ◽  
Wake Flow ◽  
Near Wake ◽  
Dominant Type ◽  
Low Speed ◽  
Flow Method ◽  
Optical Flow Method ◽  
Flow Algorithm

Abstract In this study, ability of an optical-flow algorithm in extracting wake structure of axisymmetric model was investigated. The initial data for optical-flow processing were obtained in low-speed conditions by particle image velocimetry method. The Reynolds number based on the model diameter was around ReD = 1.97 × 104 in this study. Both the time-averaged and transient flow characteristics of near-wake flow were illustrated and examined by the optical-flow analysis method proposed. The processing results of optical-flow method showed good agreement with conventional cross-correlation methods. The ability of optical-flow method to extract flow fields was, thereby, confirmed for blunt-based flow at low-speed conditions. This study showed that the antisymmetric flow behavior of the near wake is the dominant type at low-speed conditions. Differing to traditional methods and cross-correlation results, the optical-flow results showed a frequency at around StD = 0.015 of the near wake for the first time, which is connected to vortex shedding behavior of the wake flow.

Motion detection using multiscale optical flow in infrared imagery

2009 ◽  
Author(s):  
Wen-shuai Yu ◽  
Xu-chu Yu ◽  
Bing Chen ◽  
Yue Chang
Keyword(s):  
Optical Flow ◽  
Motion Detection ◽  
Infrared Imagery

scholarly journals Motion Detection Based on Optical Flow and Self-adaptive Threshold Segmentation

2011 ◽  
Vol 15 ◽  
pp. 3471-3476 ◽  
Author(s):  
Shui-gen Wei ◽  
Lei Yang ◽  
Zhen Chen ◽  
Zhen-feng Liu
Keyword(s):  
Optical Flow ◽  
Motion Detection ◽  
Adaptive Threshold ◽  
Threshold Segmentation ◽  
Self Adaptive

scholarly journals Remo Dance Motion Estimation with Markerless Motion Capture Using The Optical Flow Method

2016 ◽  
Vol 3 (1) ◽  
Author(s):  
Neny Kurniati ◽  
Achmad Basuki ◽  
Dadet Pramadihanto
Keyword(s):  
Motion Estimation ◽  
Optical Flow ◽  
Motion Capture ◽  
Motion Detection ◽  
The Body ◽  
Flow Method ◽  
Optical Flow Method ◽  
Time Motion ◽  
Markerless Motion Capture ◽  
Bounding Box

Motion capture has been developed and applied in various fields, one of them is dancing. Remo dance is a dance from East Java that tells the struggle of a prince who fought on the battlefield. Remo dancer does not use body-tight costume. He wears a few costume pieces and accessories, so required a motion detection method that can detect limb motion which does not damage the beauty of the costumes and does not interfere motion of the dancer. The method is Markerless Motion Capture. Limbs motions are partial behavior. This means that all limbs do not move simultaneously, but alternately. It required motion tracking to detect parts of the body moving and where the direction of motion. Optical flow is a method that is suitable for the above conditions. Moving body parts will be detected by the bounding box. A bounding box differential value between frames can determine the direction of the motion and how far the object is moving. The optical flow method is simple and does not require a monochrome background. This method does not use complex feature extraction process so it can be applied to real-time motion capture. Performance of motion detection with optical flow method is determined by the value of the ratio between the area of the blob and the area of the bounding box. Estimate coordinates are not necessarily like original coordinates, but if the chart of estimate motion similar to the chart of the original motion, it means motion estimation it can be said to have the same motion with the original.Keywords: Motion Capture, Markerless, Remo Dance, Optical Flow

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